43 citations · 47 across the 6 of their papers we have counts for
5 papers · 1 filter
Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library
Hiroyuki Ootomo, Rio Yokota
NVIDIA Tensor Core is a mixed-precision matrix-matrix multiplication and addition computing unit, where the theoretical peak performance is more than 300 TFlop/s on NVIDIA A100 GPU…
DGEMM on Integer Matrix Multiplication Unit
Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota
Deep learning hardware achieves high throughput and low power consumption by reducing computing precision and specializing in matrix multiplication. For machine learning inference,…
Mixed-Precision Random Projection for RandNLA on Tensor Cores
Hiroyuki Ootomo, Rio Yokota
Random projection can reduce the dimension of data while capturing its structure and is a fundamental tool for machine learning, signal processing, and information retrieval, which…
Custom 8-bit floating point value format for reducing shared memory bank conflict in approximate nearest neighbor search
Hiroyuki Ootomo, Akira Naruse
The k-nearest neighbor search is used in various applications such as machine learning, computer vision, database search, and information retrieval. While the computational cost of…
Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance
Hiroyuki Ootomo, Rio Yokota
Tensor Core is a mixed-precision matrix-matrix multiplication unit on NVIDIA GPUs with a theoretical peak performance of more than 300 TFlop/s on Ampere architectures. Tensor Cores…